IntroductionThis study investigates the relationship between retinal image features and β-amyloid (Aβ) burden in the brain with the aim of developing a noninvasive method to predict the deposition of Aβ in the brain of patients with Alzheimer's disease.MethodsRetinal images from 20 cognitively impaired and 26 cognitively unimpaired cases were acquired (3 images per subject) using a hyperspectral retinal camera. The cerebral amyloid status was determined from binary reads by a panel of 3 expert raters on 18F-florbetaben positron-emission tomography (PET) studies. Image features from the hyperspectral retinal images were calculated, including vessels tortuosity and diameter and spatial-spectral texture measures in different retinal anatomical regions.ResultsRetinal venules of amyloid-positive subjects (Aβ+) showed a higher mean tortuosity compared with the amyloid-negative (Aβ−) subjects. Arteriolar diameter of Aβ+ subjects was found to be higher than the Aβ− subjects in a zone adjacent to the optical nerve head. Furthermore, a significant difference between texture measures built over retinal arterioles and their adjacent regions were observed in Aβ+ subjects when compared with the Aβ−. A classifier was trained to automatically discriminate subjects combining the extracted features. The classifier could discern Aβ+ subjects from Aβ− subjects with an accuracy of 85%.DiscussionSignificant differences in texture measures were observed in the spectral range 450 to 550 nm which is known as the spectral region known to be affected by scattering from amyloid aggregates in the retina. This study suggests that the inclusion of metrics related to the retinal vasculature and tissue-related textures extracted from vessels and surrounding regions could improve the discrimination performance of the cerebral amyloid status.

Text summarization is a process that reduces the size of the text document and extracts significant sentences from a text document. We present a novel technique for text summarization. The originality of technique lies on exploiting local and global properties of words and identifying significant words. The local property of word can be considered as the sum of normalized term frequency multiplied by its weight and normalized number of sentences containing that word multiplied by its weight. If local score of a word is less than local score threshold, we remove that word. Global property can be thought of as maximum semantic similarity between a word and title words. Also we introduce an iterative algorithm to identify significant words. This algorithm converges to the fixed number of significant words after some iterations and the number of iterations strongly depends on the text document.  We used a two-layered backpropagation neural network with three neurons in the hidden layer to calculate weights. The results show that this technique has better performance than MS-word 2007, baseline and Gistsumm summarizers

Modularization is one of the important subjects in the software design area which leads to increasing the level of quality attributes such as maintainability, portability, reusability, interoperability and flexibility. Therefore, measuring the modularity of a designed architecture is a vital issue to obtain software with a high quality level. Moreover, low coupling between modules, high cohesion of a fine-grained module is two major criteria that could lead to more advanced standard design. In this paper, we introduce an analytical method to calculate modularity considering coupling, granularity and cohesion. To assess the comprehensiveness of the proposed method, the degree of modularity is calculated in a case study using two different architectural designs which shows the architecture's desired quality characteristics in designing the software. The assessment implies that our approach offers a holistic, flexible method considering the type of software application.

No abstract

Software architectures evaluation has an important role in the life cycle of software systems. The conceptual integrity is one of the quality attributes which could be closely related to software architectural design. It is the underlying theme or vision that unifies all levels of the system's design. In this paper, a method for measuring the conceptual integrity of software architecture is provided. Conceptual integrity measurement is done in several steps by extracting a graph structure which its nodes are architectural concepts and its edges are relationship between them. The constructed graph is then weighted according to the type of relationship among the architectural concepts. Finally, a metric for evaluating the conceptual integrity from the refined graph is provided.